1. Field of the Invention
The invention relates generally to the field of computer-aided diagnosis (CAD) including detection, characterization, diagnosis, and/or assessment of normal and diseased states (including lesions).
The present invention also generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; 5,732,697; 5,740,268; 5,790,690; 5,832,103; 5,873,824; 5,881,124; 5,931,780; 5,974,165; 5,982,915; 5,984,870; 5,987,345; 6,011,862; 6,058,322; 6,067,373; 6,075,878; 6,078,680; 6,088,473; 6,112,112; 6,138,045; 6,141,437; 6,185,320; 6,205,348; 6,240,201; 6,282,305; 6,282,307; 6,317,617; as well as U.S. patent applications Ser. Nos. 08/173,935; 08/398,307 (PCT Publication WO 96/27846); Ser. Nos. 08/536,149; 08/900,189; 09/027,468; 09/141,535; 09/471,088; 09/692,218; 09/716,335; 09/759,333; 09/760,854; 09/773,636; 09/816,217; 09/830,562; 09/818,831; 09/842,860; 09/860,574; 60/160,790; 60/176,304; 60/329,322; 09/990,311; 09/990,310; 60/332,005; and 60/331,995; as well as co-pending U.S. patent applications (listed by attorney docket number) 215752US-730-730-20, 216439US-730-730-20, and references identified in the following List of Non-Patent References by the author(s) and year of publication and cross referenced throughout the specification by reference to the respective number, in parentheses, of the reference:
List of Non-Patent References    1. Feig S A: Decreased breast cancer mortality through mammographic screening: Results of clinical trials. Radiology 167:659-665, 1988.    2. Tabar L, Fagerberg G, Duffy S W, Day N E, Gad A, Grontoft O: Update of the Swedish two-county program of mammographic screening for breast cancer. Radiol Clin North Am 30:187-210, 1992.    3. Smart C R, Hendrick R E, Rutledge J H, Smith R A: Benefit of mammography screening in women ages 40 to 49 years: Current evidence from randomized controlled trials. Cancer 75:1619-26, 1995.    4. Bassett L W, Gold R H: Breast Cancer Detection: Mammography and Other Methods in Breast Imaging New York: Grune and Stratton, 1987.    5. Kopans DB: Breast Imaging. Philadelphia: JB Lippincott, 1989.    6. Brown M L, Houn F, Sickles E A, Kessler L G: Screening mammography in community practice: positive predictive value of abnormal findings and yield of follow-up diagnostic procedures. AJR 165:1373-1377, 1995.    7. Giger M L: Computer-aided diagnosis. In: Syllabus: A Categorical Course on the Technical Aspects of Breast Imaging, edited by Haus A, Yaffe M. Oak Brook, Ill.: RSNA Publications, 1993, pp. 272-298.    8. Vyborny C J, Giger M L: Computer vision and artificial intelligence in mammography. AJR 162:699-708, 1994.    9. Giger M L, Huo Z, Kupinski M A, Vyborny C J: “Computer-aided diagnosis in mammography”, In: Handbook of Medical Imaging, Volume 2. Medical Imaging Processing and Analysis, (Sonka M, Fitzpatrick M J, eds) SPIE, pp. 915-1004, 2000.    10. D'Orsi C J, Bassett L W, Feig S A, Jackson V P, Kopans D B, Linver M N, Sickles E A, Stelling C B: Breast Imaging Reporting and Data System (BI-RADS). Reston, Va. (American College of Radiology), 1998.    11. Getty D J, Pickett R M, D'Orsi C J, Swets J A: Enhanced interpretation of diagnostic images. Invest. Radiol. 23: 240-252, 1988.    12. Swets J A, Getty D J, Pickett R M, D'Orsi C J, Seltzer S E, McNeil B J: Enhancing and evaluating diagnostic accuracy. Med Decis Making 11:9-18, 1991.    13. Cook H M, Fox M D: Application of expert systems to mammographic image analysis. American Journal of Physiologic Imaging 4: 16-22, 1989.    14. Gale A G, Roebuck E J, Riley P, Worthington B S, et al.: Computer aids to mammographic diagnosis. British Journal of Radiology 60: 887-891, 1987.    15. Getty D J, Pickett R M, D'Orsi C J, Swets J A: Enhanced interpretation of diagnostic images. Invest. Radiol. 23: 240-252, 1988.    16. Swett H A, Miller P A: ICON: A computer-based approach to differential diagnosis in radiology. Radiology 163: 555-558, 1987.    17. Huo Z, Giger M L, Vyborny C J, Bick U, Lu P, Wolverton D E, Schmidt R A: Analysis of spiculation in the computerized classification of mammographic masses” Medical Physics 22:1569-1579, 1995.    18. Jiang Y, Nishikawa R M, Wolverton D E, Giger M L, Doi K, Schmidt R A, Vyborny C J: Automated feature analysis and classification of malignant and benign clustered microcalcifications. Radiology 198(3):671-678, 1996.    19. Ackerman L V, Gose E E: Breast lesion classification by computer and xeroradiography. Breast Cancer 30:1025-1035, 1972.    20. Patrick E A, Moskowitz M, Mansukhani V T, Gruenstein E I: Expert learning system network for diagnosis of breast calcifications. Invest Radiol 16: 534-539, 1991.    21. Huo Z, Giger M L, Vyborny C J, Wolverton D E, Schmidt R A, Doi K: Automated computerized classification of malignant and benign mass lesions on digitized mammograms. Academic Radiology 5: 155-168, 1998.    22. Jiang Y, Nishikawa R M, Schmidt R A, Metz C E, Giger M L, Doi K: Improving breast cancer diagnosis with computer-aided diagnosis. Academic Radiology 6: 22-33, 1999.    23. Huo Z, Giger M L, Metz C E: Effect of dominant features on neural network performance in the classification of mammographic lesions. PMB 44: 2579-2595, 1999.    24. Huo Z, Giger M L, Vyborny C J, Wolverton D E, Metz C E: Computerized classification of benign and malignant masses on digitized mammograms: a robustness study. Academic Radiology 7:1077-1084 2000.    25. American Cancer Society. Cancer facts and Figures—1998. New York, N.Y. 1998; p. 20.    26. Metz C E. ROC methodology in radiologic imaging. Invest Radiol 1986; 21:720-733.    27. Efromovich, Sam. “Nonparametric curve estimation: methods, theory and applications”. Springer, N.Y. 1999    28. Silverman, B. W. “Density Estimation for Statistics and Data Analysis”, Chapman and Hall, London, N.Y., 1986.    29. Zhou K H, Hall W J, Shapiro D E. “Smooth non-parametric receiver operating characteristic (ROC) curves for continuous diagnostic tests”. Stat Med., 1997, 16(19):2143-56.
The following patents and patent applications may be considered relevant to the field of the present invention:    30. Doi K, Chan H-P, Giger M L: Automated systems for the detection of abnormal anatomic regions in a digital x-ray image. U.S. Pat. No. 4907156, March 1990.    31. Giger M L, Doi K, Metz C E, Yin F-F: Automated method and system for the detection and classification of abnormal lesions and parenchymal distortions in digital medical images. U.S. Pat. No. 5133020, July 1992.    32. Doi K, Matsumoto T, Giger M L, Kano A: Method and system for analysis of false positives produced by an automated scheme for the detection of lung nodules in digital chest radiographs. U.S. Pat. No. 5289374, February 1994.    33. Nishikawa R M, Giger M L, Doi K: Method for computer-aided detection of clustered microcalcifications from digital mammograms. U.S. Pat. No. 5,537,485, July 1996.    34. Giger M L, Doi K, Lu P, Huo Z: Automated method and system for improved computerized detection and classification of mass in mammograms. U.S. Pat. No. 5,832,103, November, 1998.    35. Giger M L, Bae K, Doi K: Automated method and system for the detection of lesions in medical computed tomographic scans. U.S. Pat. No. 5,881,124, March, 1999.    36. Bick U, Giger M L: Method and system for the detection of lesions in medical images. U.S. patent Allowed.    37. Giger M L, Zhang M, Lu P: Method and system for the detection of lesions and parenchymal distortions in mammograms. U.S. Pat. No. 5,657,362, August, 1997.    38. Giger M L, Kupinski M A: Automatic analysis of lesions in medical images. U.S. Pat. No. 6,138,045, Oct. 24, 2000.    39. Huo Z, Giger M L: Method and system for the computerized assessment of breast cancer risk. U.S. Pat. No. 6,282,305, Aug. 28, 2001.    40. Giger M L, Al-Hallaq H, Wolverton D E, Bick U: Method and system for the automated analysis of lesions in ultrasound images. U.S. Pat. No. 5,984,870, Nov. 16, 1999.    41. Gilhuijs K, Giger M L, Bick U: Method and system for the automated analysis of lesions in magnetic resonance images. U.S. patent Ser. No. 08/900,188 allowed.    42. Gilhuijs K, Giger M L, Bick U: Method and system for the assessment of tumor extent. U.S. patent Ser. No. 09/156,413, allowed;    43. Armato S G, Giger M L, MacMahon H: Method, system and computer readable medium for the two-dimensional and three-dimensional detection of lesions in computed tomography scans. U.S. patent Pending;    44. Giger M L, Vyborny C J, Huo Z, Lan L: Method, system and computer readable medium for an intelligent search workstation for computer assisted interpretation of medical images. U.S. patent pending, Ser. No. 09/773,636; and    45. Drukker K, Giger M L, Horsch K, Vyborny C J: Automated method and system for the detection of abnormalities in sonographic images. U.S. patent Pending Ser. No. 60/332,005.
The contents of each of these references, including patents and patent applications, are incorporated herein by reference. The techniques disclosed in the patents, patent applications and other references can be utilized as part of the present invention.